Skin cancer detection using dermoscope images

Saravanan, Shilpa; Heshma, B.; Shanofer, A.V. Ashma; Vanithamani, R.

doi:10.1016/j.matpr.2020.08.388

Cited by 18 publications

(13 citation statements)

References 6 publications

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“…One of the challenges in developing and evaluating a classification system is the availability of a solid and reliable collection of dermoscopic images [46]. In the case of melanoma, the most deadly form of skin cancer, there is a need for diverse and comprehensive datasets for training and validating artificial neural networks.…”

Section: Skin Lesion Datasetsmentioning

confidence: 99%

Melanoma image synthesis: a review using generative adversarial networks

Ahmed,

Qureshi,

Umar

et al. 2024

IJEECS

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Melanoma is a highly malignant skin cancer that may be fatal if not promptly detected and treated. The limited availability of high-quality melanoma images, which are needed for training machine learning models, is one of the obstacles to detecting melanoma. Generative adversarial networks (GANs) have grown in popularity as a strong technique for image synthesis. This research is also targeted at the sustainable development goal (SDG) for health care. In this study, we survey existing GAN-based melanoma image synthesis methods. In this work, we briefly introduce GANs and how they may be used for generating synthetic images. Ensuring healthy lifestyles and promoting well-being for everyone, regardless of age, is the main aim. A comparative study is carried out on how GANs are used in current research to generate melanoma images and how they improve the classification performance of neural networks. Various public and proprietary datasets for training GANs in melanoma image synthesis are also discussed. Lastly, we assess the examined studies' performance using measures like the Frechet Inception distance (FID), Inception score, structural similarity ındex (SSIM), and various classification performance metrics. We compare the evaluated findings and suggest further GAN-based melanoma image-creation research.

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Section: Skin Lesion Datasetsmentioning

confidence: 99%

Melanoma image synthesis: a review using generative adversarial networks

Ahmed,

Qureshi,

Umar

et al. 2024

IJEECS

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show abstract

“…The system described can segment skin images using a semi-supervised approach shift mean algorithm it does not require mentioning the cluster’s numbers [ 65 ]. The threshold-based segmentation technique is utilized, as the sample is split into many regions depending on the threshold merit so that the edges of the cancerous area become clear [ 66 ]. A skin lesion segmentation technique was designed that established adaptive thresholding with the normalization of color networks for dermoscopic images [ 67 ].…”

Section: Skin Cancer Recognition and Classification Systemmentioning

confidence: 99%

“… Segmentation Results Concerning the Accuracy [ 50 , 60 , 61 , 62 , 63 , 66 , 68 , 72 , 73 , 74 , 75 , 77 , 80 , 85 ]. …”

Section: Figurementioning

confidence: 99%

Skin Lesion Analysis and Cancer Detection Based on Machine/Deep Learning Techniques: A Comprehensive Survey

Zafar

Sharif

et al. 2023

Life

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The skin is the human body’s largest organ and its cancer is considered among the most dangerous kinds of cancer. Various pathological variations in the human body can cause abnormal cell growth due to genetic disorders. These changes in human skin cells are very dangerous. Skin cancer slowly develops over further parts of the body and because of the high mortality rate of skin cancer, early diagnosis is essential. The visual checkup and the manual examination of the skin lesions are very tricky for the determination of skin cancer. Considering these concerns, numerous early recognition approaches have been proposed for skin cancer. With the fast progression in computer-aided diagnosis systems, a variety of deep learning, machine learning, and computer vision approaches were merged for the determination of medical samples and uncommon skin lesion samples. This research provides an extensive literature review of the methodologies, techniques, and approaches applied for the examination of skin lesions to date. This survey includes preprocessing, segmentation, feature extraction, selection, and classification approaches for skin cancer recognition. The results of these approaches are very impressive but still, some challenges occur in the analysis of skin lesions because of complex and rare features. Hence, the main objective is to examine the existing techniques utilized in the discovery of skin cancer by finding the obstacle that helps researchers contribute to future research.

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“…Lesions that look like benign skin lesions might make melanoma challenging to diagnose. Asymmetry, border, color, diameter, and evolution (ABCDE) criteria, biopsy, and histological investigation are used by most dermatologists [ 12 , 13 , 14 , 15 , 16 , 17 ]. Manual visualization and segmentation for pattern analysis make these methods time-consuming, expensive, and inaccurate [ 18 ].…”

Section: Introductionmentioning

confidence: 99%

An Improved Skin Lesion Classification Using a Hybrid Approach with Active Contour Snake Model and Lightweight Attention-Guided Capsule Networks

Behara,

Bhero,

Agee

2024

Diagnostics

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Skin cancer is a prevalent type of malignancy on a global scale, and the early and accurate diagnosis of this condition is of utmost importance for the survival of patients. The clinical assessment of cutaneous lesions is a crucial aspect of medical practice, although it encounters several obstacles, such as prolonged waiting time and misinterpretation. The intricate nature of skin lesions, coupled with variations in appearance and texture, presents substantial barriers to accurate classification. As such, skilled clinicians often struggle to differentiate benign moles from early malignant tumors in skin images. Although deep learning-based approaches such as convolution neural networks have made significant improvements, their stability and generalization continue to experience difficulties, and their performance in accurately delineating lesion borders, capturing refined spatial connections among features, and using contextual information for classification is suboptimal. To address these limitations, we propose a novel approach for skin lesion classification that combines snake models of active contour (AC) segmentation, ResNet50 for feature extraction, and a capsule network with a fusion of lightweight attention mechanisms to attain the different feature channels and spatial regions within feature maps, enhance the feature discrimination, and improve accuracy. We employed the stochastic gradient descent (SGD) optimization algorithm to optimize the model’s parameters. The proposed model is implemented on publicly available datasets, namely, HAM10000 and ISIC 2020. The experimental results showed that the proposed model achieved an accuracy of 98% and AUC-ROC of 97.3%, showcasing substantial potential in terms of effective model generalization compared to existing state-of-the-art (SOTA) approaches. These results highlight the potential for our approach to reshape automated dermatological diagnosis and provide a helpful tool for medical practitioners.

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Skin cancer detection using dermoscope images

Cited by 18 publications

References 6 publications

Melanoma image synthesis: a review using generative adversarial networks

Melanoma image synthesis: a review using generative adversarial networks

Skin Lesion Analysis and Cancer Detection Based on Machine/Deep Learning Techniques: A Comprehensive Survey

An Improved Skin Lesion Classification Using a Hybrid Approach with Active Contour Snake Model and Lightweight Attention-Guided Capsule Networks

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